OpenRaiser/Intern-Atlas huggingface dataset:
Image Reconstruction using Matched Wavelet Estimated from Data Sensed Compressively using Partial Canonical Identity Matrix
Venue: arXiv cs.CV 2017
Year: 2017

This paper proposes a joint framework wherein lifting-based, separable, image-matched wavelets are estimated from compressively sensed (CS) images and used for the reconstruction of the same. Matched wavelet can be easily designed if full image is available. Also matched wavelet may provide better reconstruction results in CS application compared to standard wavelet sparsifying basis. Since in CS application, we have compressively sensed image instead of full image, existing methods of designing matched wavelet cannot be used. Thus, we propose a joint framework that estimates matched wavelet from the compressively sensed images and also reconstructs full images. This paper has three significant contributions. First, lifting-based, image-matched separable wavelet is designed from compressively sensed images and is also used to reconstruct the same. Second, a simple sensing matrix is employed to sample data at sub-Nyquist rate such that sensing and reconstruction time is reduced considerably without any noticeable degradation in the reconstruction performance. Third, a new multi-level L-Pyramid wavelet decomposition strategy is provided for separable wavelet implementation on images that leads to improved reconstruction performance. Compared to CS-based reconstruction using standard wavelets with Gaussian sensing matrix and with existing wavelet decomposition strategy, the proposed methodology provides faster and better image reconstruction in compressive sensing application.
Robust Regularized ZF in Cooperative Broadcast Channel under Distributed CSIT
Venue: arXiv cs.IT 2017
Year: 2017

In this work, we consider the sum rate performance of joint processing coordinated multi-point transmission network (JP-CoMP, a.k.a Network MIMO) in a so-called distributed channel state information (D-CSI) setting. In the D-CSI setting, the various transmitters (TXs) acquire a local, TX-dependent, estimate of the global multi-user channel state matrix obtained via terminal feedback and limited backhauling. The CSI noise across TXs can be independent or correlated, so as to reflect the degree to which TXs can exchange information over the backhaul, hence allowing to model a range of situations bridging fully distributed and fully centralized CSI settings. In this context we aim to study the price of CSI distributiveness in terms of sum rate at finite SNR when compared with conventional centralized scenarios. We consider the family of JP-CoMP precoders known as regularized zero-forcing (RZF). We conduct our study in the large scale antenna regime, as it is currently envisioned to be used in real 5G deployments. It is then possible to obtain accurate approximations on so-called deterministic equivalents of the signal to interference and noise ratios. Guided by the obtained deterministic equivalents, we propose an approach to derive a RZF scheme that is robust to the distributed aspect of the CSI, whereby the key idea lies in the optimization of a TX-dependent power level and regularization factor. Our analysis confirms the improved robustness of the proposed scheme with respect to CSI inconsistency at different TXs, even with moderate number of antennas and receivers (RXs).
Performance Control of Tendon-Driven Endoscopic Surgical Robots With Friction and Hysteresis
Venue: arXiv cs.RO 2017
Year: 2017

In this study, a new position control scheme for the tendon-sheath mechanism (TSM) which is used in flexible medical devices is presented. TSM is widely used in dexterous robotic applications because it can flexibly work in limited space, in constrained environments, and provides efficient power transmission from the external actuator to the distal joint. However, nonlinearities from friction and backlash hysteresis between the tendon and the sheath pose challenges in achieving precise position controls of the end effector. Previous studies on the TSM only address the control problem under the assumptions of known tendon-sheath configuration and known model parameters of the backlash hysteresis nonlinearity. These approaches can have adverse impact and limitations on the overall system performances and practical implementation. This paper presents a new approach to model and control the TSM-driven flexible robotic systems. The designed controller does not require exact knowledge of nonlinear friction and backlash hysteresis parameters, only their bounds are online estimated. Simulation and experimental validation results show that the proposed control scheme can significantly improve the tracking performances without the presence of the exact knowledge of the model parameters and the sheath configuration.
Characterisation of speech diversity using self-organising maps
Venue: arXiv cs.CL 2017
Year: 2017

We report investigations into speaker classification of larger quantities of unlabelled speech data using small sets of manually phonemically annotated speech. The Kohonen speech typewriter is a semi-supervised method comprised of self-organising maps (SOMs) that achieves low phoneme error rates. A SOM is a 2D array of cells that learn vector representations of the data based on neighbourhoods. In this paper, we report a method to evaluate pronunciation using multilevel SOMs with /hVd/ single syllable utterances for the study of vowels, for Australian pronunciation.
Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images
Venue: arXiv cs.CV 2017
Year: 2017

The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.
Intersections and Unions of Session Types
Venue: arXiv cs.PL 2017
Year: 2017

Prior work has extended the deep, logical connection between the linear sequent calculus and session-typed message-passing concurrent computation with equi-recursive types and a natural notion of subtyping. In this paper, we extend this further by intersection and union types in order to express multiple behavioral properties of processes in a single type. We prove session fidelity and absence of deadlock and illustrate the expressive power of our system with some simple examples. We observe that we can represent internal and external choice by intersection and union, respectively, which was previously suggested by Padovani for a different language of session types motivated by operational rather than logical concerns.
Generating Code with Polymorphic let: A Ballad of Value Restriction, Copying and Sharing
Venue: arXiv cs.PL 2017
Year: 2017

Getting polymorphism and effects such as mutation to live together in the same language is a tale worth telling, under the recurring refrain of copying vs. sharing. We add new stanzas to the tale, about the ordeal to generate code with polymorphism and effects, and be sure it type-checks. Generating well-typed-by-construction polymorphic let-expressions is impossible in the Hindley-Milner type system: even the author believed that. The polymorphic-let generator turns out to exist. We present its derivation and the application for the lightweight implementation of quotation via a novel and unexpectedly simple source-to-source transformation to code-generating combinators. However, generating let-expressions with polymorphic functions demands more than even the relaxed value restriction can deliver. We need a new deal for let-polymorphism in ML. We conjecture the weaker restriction and implement it in a practically-useful code-generation library. Its formal justification is formulated as the research program.
GADTs and Exhaustiveness: Looking for the Impossible
Venue: arXiv cs.PL 2017
Year: 2017

Sound exhaustiveness checking of pattern-matching is an essential feature of functional programming languages, and OCaml supports it for GADTs. However this check is incomplete, in that it may fail to detect that a pattern can match no concrete value. In this paper we show that this problem is actually undecidable, but that we can strengthen the exhaustiveness and redundancy checks so that they cover more practical cases. The new algorithm relies on a clever modification of type inference for patterns.
Guided Optical Flow Learning
Venue: arXiv cs.CV 2017
Year: 2017

We study the unsupervised learning of CNNs for optical flow estimation using proxy ground truth data. Supervised CNNs, due to their immense learning capacity, have shown superior performance on a range of computer vision problems including optical flow prediction. They however require the ground truth flow which is usually not accessible except on limited synthetic data. Without the guidance of ground truth optical flow, unsupervised CNNs often perform worse as they are naturally ill-conditioned. We therefore propose a novel framework in which proxy ground truth data generated from classical approaches is used to guide the CNN learning. The models are further refined in an unsupervised fashion using an image reconstruction loss. Our guided learning approach is competitive with or superior to state-of-the-art approaches on three standard benchmark datasets yet is completely unsupervised and can run in real time.
Sensing as a Service (S2aaS): Buying and Selling IoT Data
Venue: arXiv cs.NI 2017
Year: 2017

The Internet of Things (IoT) [1] envisions the creation of an environment where everyday objects (e.g. microwaves, fridges, cars, coffee machines, etc.) are connected to the internet and make users' lives more convenient. It will also lead users to consume resources more efficiently.
An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

We propose a method for semi-supervised training of structured-output neural networks. Inspired by the framework of Generative Adversarial Networks (GAN), we train a discriminator network to capture the notion of a quality of network output. To this end, we leverage the qualitative difference between outputs obtained on the labelled training data and unannotated data. We then use the discriminator as a source of error signal for unlabelled data. This effectively boosts the performance of a network on a held out test set. Initial experiments in image segmentation demonstrate that the proposed framework enables achieving the same network performance as in a fully supervised scenario, while using two times less annotations.
On Optimization Modulo Theories, MaxSMT and Sorting Networks
Venue: arXiv cs.LO 2017
Year: 2017

Optimization Modulo Theories (OMT) is an extension of SMT which allows for finding models that optimize given objectives. (Partial weighted) MaxSMT --or equivalently OMT with Pseudo-Boolean objective functions, OMT+PB-- is a very-relevant strict subcase of OMT. We classify existing approaches for MaxSMT or OMT+PB in two groups: MaxSAT-based approaches exploit the efficiency of state-of-the-art MAXSAT solvers, but they are specific-purpose and not always applicable; OMT-based approaches are general-purpose, but they suffer from intrinsic inefficiencies on MaxSMT/OMT+PB problems. We identify a major source of such inefficiencies, and we address it by enhancing OMT by means of bidirectional sorting networks. We implemented this idea on top of the OptiMathSAT OMT solver. We run an extensive empirical evaluation on a variety of problems, comparing MaxSAT-based and OMT-based techniques, with and without sorting networks, implemented on top of OptiMathSAT and νZ. The results support the effectiveness of this idea, and provide interesting insights about the different approaches.
Propagation via Kernelization: The Vertex Cover Constraint
Venue: arXiv cs.AI 2017
Year: 2017

The technique of kernelization consists in extracting, from an instance of a problem, an essentially equivalent instance whose size is bounded in a parameter k. Besides being the basis for efficient param-eterized algorithms, this method also provides a wealth of information to reason about in the context of constraint programming. We study the use of kernelization for designing propagators through the example of the Vertex Cover constraint. Since the classic kernelization rules often correspond to dominance rather than consistency, we introduce the notion of "loss-less" kernel. While our preliminary experimental results show the potential of the approach, they also show some of its limits. In particular, this method is more effective for vertex covers of large and sparse graphs, as they tend to have, relatively, smaller kernels.
Two-Dimensional AoD and AoA Acquisition for Wideband mmWave Systems with Cross-Polarized MIMO
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, a novel two-dimensional super-resolution angle-of-departure (AoD) and angle-of-arrival (AoA) estimation technique is proposed for wideband millimeter-wave multiple-input multiple-output systems with cross-polarized antenna elements. The key ingredient of the proposed method is to form custom designed beam pairs, and devise an invertible function of the AoD/AoA to be estimated from the corresponding beam pairs. Further, a new multi-layer reference signal structure is developed for the proposed method to facilitate angle estimation for wideband channels with cross-polarized antenna elements. To facilitate feedback in closed-loop frequency division duplexing systems, a novel differential feedback strategy is proposed aiming at feedback reduction for the two-dimensional angle estimation. Numerical results demonstrate that by using the proposed method, good azimuth/elevation AoD and AoA estimation performance can be achieved under different levels of signal-to-noise ratio, channel conditions, and antenna array configurations.
Deep Generalized Canonical Correlation Analysis
Venue: arXiv cs.LG 2017
Year: 2017

We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a method for learning nonlinear transformations of arbitrarily many views of data, such that the resulting transformations are maximally informative of each other. While methods for nonlinear two-view representation learning (Deep CCA, (Andrew et al., 2013)) and linear many-view representation learning (Generalized CCA (Horst, 1961)) exist, DGCCA is the first CCA-style multiview representation learning technique that combines the flexibility of nonlinear (deep) representation learning with the statistical power of incorporating information from many independent sources, or views. We present the DGCCA formulation as well as an efficient stochastic optimization algorithm for solving it. We learn DGCCA representations on two distinct datasets for three downstream tasks: phonetic transcription from acoustic and articulatory measurements, and recommending hashtags and friends on a dataset of Twitter users. We find that DGCCA representations soundly beat existing methods at phonetic transcription and hashtag recommendation, and in general perform no worse than standard linear many-view techniques.
Soft Biometrics: Gender Recognition from Unconstrained Face Images using Local Feature Descriptor
Venue: arXiv cs.CV 2017
Year: 2017

Gender recognition from unconstrained face images is a challenging task due to the high degree of misalignment, pose, expression, and illumination variation. In previous works, the recognition of gender from unconstrained face images is approached by utilizing image alignment, exploiting multiple samples per individual to improve the learning ability of the classifier, or learning gender based on prior knowledge about pose and demographic distributions of the dataset. However, image alignment increases the complexity and time of computation, while the use of multiple samples or having prior knowledge about data distribution is unrealistic in practical applications. This paper presents an approach for gender recognition from unconstrained face images. Our technique exploits the robustness of local feature descriptor to photometric variations to extract the shape description of the 2D face image using a single sample image per individual. The results obtained from experiments on Labeled Faces in the Wild (LFW) dataset describe the effectiveness of the proposed method. The essence of this study is to investigate the most suitable functions and parameter settings for recognizing gender from unconstrained face images.
Backpropagation Training for Fisher Vectors within Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Fisher-Vectors (FV) encode higher-order statistics of a set of multiple local descriptors like SIFT features. They already show good performance in combination with shallow learning architectures on visual recognitions tasks. Current methods using FV as a feature descriptor in deep architectures assume that all original input features are static. We propose a framework to jointly learn the representation of original features, FV parameters and parameters of the classifier in the style of traditional neural networks. Our proof of concept implementation improves the performance of FV on the Pascal Voc 2007 challenge in a multi-GPU setting in comparison to a default SVM setting. We demonstrate that FV can be embedded into neural networks at arbitrary positions, allowing end-to-end training with back-propagation.
Design of Distributed Voting Systems
Venue: arXiv cs.CR 2017
Year: 2017

Countries like Estonia, Norway or Australia developed electronic voting systems, which could be used to realize parliamentary elections with the help of personal computers and the Internet. These systems are completely different in their design and their way to solve the same problem. In this thesis, we analyze some of the largest real-world systems, describe their building blocks and their general design to focus on possible problems in these electronic voting systems. Furthermore, we present a template for an e-voting system, which we designed to try to fulfill the preliminaries and requirements of a secure electronic voting system. We use the experiences and the building blocks of existing systems to combine them to another more secure system. Afterwards, we compare our concept with real-world systems to evaluate the fulfillments of the requirements. Conclusively, we discuss the occurring problems when designing a secure system. Peer-to-peer networks provide many advantages, like decentralization, which might be applicable to electronic voting systems. Therefore, we take a look on the distributed database called blockchain and the usage in a peer-to-peer voting system. Our contribution to this topic is a modification of the proof-of-stake, which enables the usage of common devices, like smartphones or tablets, for the blockchain verification and inclusion of new ballots to the chain. This proof does not need much computing power and has a lower carbon footprint than the proof-of-work in the Bitcoin protocol.
Predicting Audience's Laughter Using Convolutional Neural Network
Venue: arXiv cs.CL 2017
Year: 2017

For the purpose of automatically evaluating speakers' humor usage, we build a presentation corpus containing humorous utterances based on TED talks. Compared to previous data resources supporting humor recognition research, ours has several advantages, including (a) both positive and negative instances coming from a homogeneous data set, (b) containing a large number of speakers, and (c) being open. Focusing on using lexical cues for humor recognition, we systematically compare a newly emerging text classification method based on Convolutional Neural Networks (CNNs) with a well-established conventional method using linguistic knowledge. The advantages of the CNN method are both getting higher detection accuracies and being able to learn essential features automatically.
Flashield: a Key-value Cache that Minimizes Writes to Flash
Venue: arXiv cs.OS 2017
Year: 2017

As its price per bit drops, SSD is increasingly becoming the default storage medium for cloud application databases. However, it has not become the preferred storage medium for key-value caches, even though SSD offers more than 10x lower price per bit and sufficient performance compared to DRAM. This is because key-value caches need to frequently insert, update and evict small objects. This causes excessive writes and erasures on flash storage, since flash only supports writes and erasures of large chunks of data. These excessive writes and erasures significantly shorten the lifetime of flash, rendering it impractical to use for key-value caches. We present Flashield, a hybrid key-value cache that uses DRAM as a "filter" to minimize writes to SSD. Flashield performs light-weight machine learning profiling to predict which objects are likely to be read frequently before getting updated; these objects, which are prime candidates to be stored on SSD, are written to SSD in large chunks sequentially. In order to efficiently utilize the cache's available memory, we design a novel in-memory index for the variable-sized objects stored on flash that requires only 4 bytes per object in DRAM. We describe Flashield's design and implementation and, we evaluate it on a real-world cache trace. Compared to state-of-the-art systems that suffer a write amplification of 2.5x or more, Flashield maintains a median write amplification of 0.5x without any loss of hit rate or throughput.
Energy Saving Additive Neural Network
Venue: arXiv cs.NE 2017
Year: 2017

In recent years, machine learning techniques based on neural networks for mobile computing become increasingly popular. Classical multi-layer neural networks require matrix multiplications at each stage. Multiplication operation is not an energy efficient operation and consequently it drains the battery of the mobile device. In this paper, we propose a new energy efficient neural network with the universal approximation property over space of Lebesgue integrable functions. This network, called, additive neural network, is very suitable for mobile computing. The neural structure is based on a novel vector product definition, called ef-operator, that permits a multiplier-free implementation. In ef-operation, the "product" of two real numbers is defined as the sum of their absolute values, with the sign determined by the sign of the product of the numbers. This "product" is used to construct a vector product in $R^N$. The vector product induces the $l_1$ norm. The proposed additive neural network successfully solves the XOR problem. The experiments on MNIST dataset show that the classification performances of the proposed additive neural networks are very similar to the corresponding multi-layer perceptron and convolutional neural networks (LeNet).